Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Combining Partial True Discovery Guarantee Procedures.

Closed testing has recently been shown to be optimal for simultaneous true discovery proportion control. It is, however, challenging to construct true discovery guarantee procedures in such a way that it focuses power on some feature sets chosen by users based on their specific interest or expertise. We propose a procedure that allows users to target power on prespecified feature sets, that is, "focus sets." Still, the method also allows inference for feature sets chosen post hoc, that is, "nonfocus sets," for which we deduce a true discovery lower confidence bound by interpolation. Our procedure is built from partial true discovery guarantee procedures combined with Holm's procedure and is a conservative shortcut to the closed testing procedure. A simulation study confirms that the statistical power of our method is relatively high for focus sets, at the cost of power for nonfocus sets, as desired. In addition, we investigate its power property for sets with specific structures, for example, trees and directed acyclic graphs. We also compare our method with AdaFilter in the context of replicability analysis. The application of our method is illustrated with a gene ontology analysis in gene expression data.

Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction.

Motivated by improving the prediction of the human immunodeficiency virus (HIV) suppression status using electronic health records (EHR) data, we propose a functional multivariable logistic regression model, which accounts for the longitudinal binary process and continuous process simultaneously. Specifically, the longitudinal measurements for either binary or continuous variables are modeled by functional principal components analysis, and their corresponding functional principal component scores are used to build a logistic regression model for prediction. The longitudinal binary data are linked to underlying Gaussian processes. The estimation is done using penalized spline for the longitudinal continuous and binary data. Group-lasso is used to select longitudinal processes, and the multivariate functional principal components analysis is proposed to revise functional principal component scores with the correlation. The method is evaluated via comprehensive simulation studies and then applied to predict viral suppression using EHR data for people living with HIV in South Carolina.

Bayesian inference for multivariate probit model with latent envelope.

The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.

Differential recall bias in estimating treatment effects in observational studies.

Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood.

Comparing the accuracy of intraocular lens power calculation formulas using artificial intelligence and traditional formulas in highly myopic patients: a meta-analysis.

PURPOSE: The accuracy of intraocular lens (IOL) calculations is one of the key indicators for determining the success of cataract surgery. However, in highly myopic patients, the calculation errors are relatively larger than those in general patients. With the continuous development of artificial intelligence (AI) technology, there has also been a constant emergence of AI-related calculation formulas. The purpose of this investigation was to evaluate the accuracy of AI calculation formulas in calculating the power of IOL for highly myopic patients. METHODS: We searched the relevant literature through August 2023 using three databases: PubMed, EMBASE, and the Cochrane Library. Six IOL calculation formulas were compared: Kane, Hill-RBF, EVO, Barrett II, Haigis, and SRK/T. The included metrics were the mean absolute error (MAE) and percentage of errors within ± 0.25 D, ± 0.50 D, and ± 1.00 D. RESULTS: The results showed that the MAE of Kane was significantly lower than that of Barrett II (mean difference = - 0.03 D, P = 0.02), SRK/T (MD = - 0.08 D, P = 0.02), and Haigis (MD = - 0.12 D, P < 0.00001). The percentage refractive prediction errors for Kane at ± 0.25 D, ± 0.50 D, and ± 1.00 D were significantly greater than those for SRK/T (P = 0.007, 0.003, and 0.01, respectively) and Haigis (P = 0.009, 0.0001, and 0.001, respectively). No statistically significant differences were noted between Hill-RBF and Barret, but Hill-RBF was significantly better than SRK/T and Haigis. CONCLUSION: The AI calculation formulas showed more accurate results compared with traditional formulas. Among them, Kane has the best performance in calculating IOL degrees for highly myopic patients.

Accuracy of intra ocular lens calculation formulae in patients with pseudoexfoliation syndrome.

BACKGROUND: The purpose of this study was to investigate the visual and refractive outcomes in patients with pseudoexfoliation (PXF) undergoing routine cataract surgery and to compare the accuracy of intraocular lens (IOL) power calculation formulae. METHODS: Retrospective case-series study from Shamir medical center, a public hospital, Israel. Medical records of patients who underwent routine cataract surgery between January 2019 and August 2021 were investigated. Postoperative visual acuity and manifest refraction were examined. The error in predicted refraction and IOL power calculation accuracy within a range of ± 0.50 to ± 1.00 diopters were compared between different IOL calculating formulae. RESULTS: 151 eyes of 151 patients ages 73.9 ± 7.1 years were included in this study- 58 eyes in the PXF group and 93 eyes in the control group. The mean absolute error (MAE) for the BUII formula was 0.63D ± 0.87 for the PXF group and 0.36D ± 0.48 for the control group (p < 0.05). The MAE for the Hill-RBF 3.0 formula was 0.61D ± 0.84 for the PXF group and 0.42D ± 0.55 for the control group (p = 0.05). There were significant differences in MAE and MedAE between PXF group and control group measures (p < 0.05). In the PXF group there were no significant differences between the different formulae. CONCLUSIONS: There were significant differences in accuracy of IOL power calculations in all formulae between PXF group and control group measures. PXF patients show hyperopic shift from predicted refraction. Barret universal II formula had the highest proportion of eyes with absolute error in prediction below or equal to 0.50 D in both PXF and control groups.

Evaluation of anterior segment parameters between pregnancy trimesters and postpartum with pentacam scheimflug imaging: a prospective study.

PURPOSE: To evaluate the effect of pregnancy on the anterior chamber, corneal parameter, and intraocular pressure measurements; and compare the results between trimesters, postpartum and non-pregnant healthy age-matched women. METHODS: This prospective study included 41 pregnant women and 53 non-pregnant women. Four measurements were taken from the pregnant women, in each trimester and postpartum third month, and once from the control group. Of the individuals included in the study, anterior chamber depth (ACD), anterior chamber volume (ACV), K1 (flat keratometry), K2 (steep keratometry), Kmean (mean value of K1 and K2), anterior chamber angle (ACA), central corneal thickness (CCT), thinnest corneal thickness (TCT), astigmatism value (AST), corneal volume (CV), biometry, axial length (AL), spherical equivalent (SFEQ), intraocular lens power (ILP), VA (visual acuity) datas were recorded. RESULTS: We observed a statistically significant decrease in K2, CCT, ACD, AL and CV in the postpartum period (p = 0.025, p < 0.001, p = 0.029, p = 0.005, p = 0.004 respectively) and a statistically significant increase in ACV, CCT, and TCT as the gestational week progressed in the pregnant group (p = 0.007, p < 0.001, p = 0.025, respectively). A statistically significant decrease in IOP towards to the third trimester, and an increase in the postpartum period was observed (p < 0.001). We did not observe statistically significant changes in K1, Kmean, AST, ACA, VA, ILP, and SFEQ values. CONCLUSION: It is important to investigate the physiological changes that may occur during pregnancy, distinguish them from pathological changes, and avoid unnecessary treatment. We consider that it's also important to guide the timing of anterior segment surgeries such as cataract and refractive surgery and to prescribe glasses/contact lenses.

Causal inference in the absence of positivity: The role of overlap weights.

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators. We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population. Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.

Accuracy of 12 Intraocular Lens Power Formulas After Corneal Myopic Refractive Surgery.

PURPOSE: To assess the predictive accuracy of new-generation online intraocular lens (IOL) power formulas in eyes with previous myopic laser refractive surgery (LRS) and to evaluate the influence of corneal asphericity on the predictive accuracy. METHODS: The authors retrospectively evaluated 52 patients (78 eyes) with a history of laser in situ keratomileusis (LASIK) or photorefractive keratectomy (PRK) who subsequently underwent cataract surgery. Refractive prediction errors were calculated for 12 no-history new online formulas: 8 formulas with post-LRS versions (Barrett True-K, EVO 2.0, Hoffer QST, and Pearl DGS) using keratometry and posterior/total keratometry measured by IOLMaster 700 and 4 formulas without post-LRS versions (Cooke K6 and Kane) using keratometry and total keratometry. The refractive prediction error, mean absolute error (MAE), and percentages of eyes with prediction errors of ±0.25, ±0.50, ±0.75, ±1.00, and ±1.50 diopters (D) were compared. RESULTS: The MAEs of the 12 formulas were significantly different (F = 83.66, P < .001). The MAEs ranged from 0.62 to 0.94 D and from 1.07 to 1.84 D in the formulas with and without post-LRS versions, respectively. The EVO formula produced the lowest MAE (0.60) and MedAE (0.47), followed by the Barrett True-K (0.69 and 0.50, respectively). Each percentage of eyes with refractive prediction error was also significantly different among the 12 formulas (P < .001). CONCLUSIONS: The EVO and Barrett True-K formulas demonstrate comparable performance to the other existing formulas in eyes with a history of myopic LASIK/PRK. Surgeons should use these formulas with post-LRS versions and input keratometric values whenever possible. [J Refract Surg. 2024;40(6):e354-e361.].

Doubly robust estimation and sensitivity analysis for marginal structural quantile models.

The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding.

A Novel Multimodal Biometric Person Authentication System Based on ECG and Iris Data.

Existing security issues like keys, pins, and passwords employed presently in almost all the fields that have certain limitations like passwords and pins can be easily forgotten; keys can be lost. To overcome such security issues, new biometric features have shown outstanding improvements in authentication systems as a result of significant developments in biological digital signal processing. Currently, the multimodal authentications have gained huge attention in biometric systems which can be either behavioural or physiological. A biometric system with multimodality club data from many biometric modalities increases each biometric system's performance and makes it more resistant to spoof attempts. Apart from electrocardiogram (ECG) and iris, there are a lot of other biometric traits that can be captured from the human body. They include face, fingerprint, gait, keystroke dynamics, voice, DNA, palm vein, and hand geometry recognition. Electrocardiograms (ECG) have recently been employed in unimodal and multimodal biometric recognition systems as a novel biometric technology. When compared to other biometric approaches, ECG has the intrinsic quality of a person's liveness, making it difficult to fake. Similarly, the iris also plays an important role in biometric authentication. Based on these assumptions, we present a multimodal biometric person authentication system. The projected method includes preprocessing, segmentation, feature extraction, feature fusion, and ensemble classifier where majority voting is presented to obtain the final outcome. The comparative analysis shows the overall performance as 96.55%, 96.2%, 96.2%, 96.5%, and 95.65% in terms of precision, F1-score, sensitivity, specificity, and accuracy.

On exact randomization-based covariate-adjusted confidence intervals.

Randomization-based inference using the Fisher randomization test allows for the computation of Fisher-exact P-values, making it an attractive option for the analysis of small, randomized experiments with non-normal outcomes. Two common test statistics used to perform Fisher randomization tests are the difference-in-means between the treatment and control groups and the covariate-adjusted version of the difference-in-means using analysis of covariance. Modern computing allows for fast computation of the Fisher-exact P-value, but confidence intervals have typically been obtained by inverting the Fisher randomization test over a range of possible effect sizes. The test inversion procedure is computationally expensive, limiting the usage of randomization-based inference in applied work. A recent paper by Zhu and Liu developed a closed form expression for the randomization-based confidence interval using the difference-in-means statistic. We develop an important extension of Zhu and Liu to obtain a closed form expression for the randomization-based covariate-adjusted confidence interval and give practitioners a sufficiency condition that can be checked using observed data and that guarantees that these confidence intervals have correct coverage. Simulations show that our procedure generates randomization-based covariate-adjusted confidence intervals that are robust to non-normality and that can be calculated in nearly the same time as it takes to calculate the Fisher-exact P-value, thus removing the computational barrier to performing randomization-based inference when adjusting for covariates. We also demonstrate our method on a re-analysis of phase I clinical trial data.

Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.

Prediction of low-addition segmented refractive intraocular lens position and deviation using anterior-segment optical coherence tomography.

This study aimed to develop and analyze the accuracy of predictive formulae for postoperative anterior chamber depth, tilt, and decentration of low-added-segment refractive intraocular lenses. This single-center, retrospective, observational study included the right eyes of 96 patients (mean age: 72.43 ± 6.58 years), who underwent a cataract surgery with implantation of a low-added segmented refractive intraocular lens at the Medical University Hospital between July 2019 and January 2021, and were followed up for more than 1 month postoperatively. The participants were divided into an estimation group to create a prediction formula and a validation group to verify the accuracy of the formula. Anterior segment optical coherence tomography (CASIA 2, Tomey Corporation, Japan) and swept-source optical coherence tomography biometry (IOL Master 700, Carl Zeiss Meditec AG) were used to measure the anterior ocular components. A predictive formula was devised for postoperative anterior chamber depth, intraocular lens tilt, and intraocular lens decentration (p <0.01) in the estimation group. A significant positive correlation was observed between the estimated values calculated using the prediction formula and the measured values for postoperative anterior chamber depth (r = 0.792), amount of intraocular lens tilt (r = 0.610), direction of intraocular lens tilt (r = 0.668), and amount of intraocular lens decentration (r = 0.431) (p < 0.01) in the validation group. In conclusion, our findings reveal that predicting the position of the low-added segmented refractive intraocular lens enables the prognosis of postoperative refractive values with a greater accuracy in determining the intraocular lens adaptation.

Effects of intraocular pressure change on intraocular lens power calculation in primary open-angle glaucoma and ocular hypertension.

This study aimed to assess the effect of intraocular pressure (IOP) changes on biometry and intraocular lens (IOL) power calculation in patients diagnosed with primary open-angle glaucoma (POAG) and ocular hypertension (OHT). This prospective non-randomized cohort study enrolled patients with diagnosed POAG and OHT, presenting with IOP levels exceeding 25 mmHg. Thai Clinical Trials Registry number was TCTR20180912007. Optical biometry, encompassing measurements such as corneal thickness (CCT), keratometry, anterior chamber depth (ACD), and axial length, was conducted before and after IOP reduction. The IOL power was also determined using the SRK/T formula. The main outcomes measured were alterations in biometry and IOL power. Correlations between IOP, biometric parameters, and IOL power were analyzed. In total, 28 eyes were included in the study, with a mean patient age of 65.71±10.2 years. After IOP reduction, all biometric parameters, except CCT and ACD, exhibited a decrease without reaching statistical significance (all p>0.05). Meanwhile, IOL power showed a slight increase of 0.214±0.42 diopters (P = 0.035). The correlation between IOP and biometric parameters was found to be weak. However, there was a moderate correlation between IOP and IOL power (r2 = 0.267). Notably, IOL power tended to increase by more than 0.5 diopters when IOP decreased by more than 10 mmHg (p < 0.001). In conclusion, changes in IOP among patients with POAG and OHT do not significantly impact biometry and IOL power calculations. Nonetheless, it may be prudent to consider a slight adjustment in IOL power when IOP is lowered by more than 10 mmHg.

A New Method to Minimize the Standard Deviation and Root Mean Square of the Prediction Error of Single-Optimized IOL Power Formulas.

Purpose: The purpose of this study was to develop a simplified method to approximate constants minimizing the standard deviation (SD) and the root mean square (RMS) of the prediction error in single-optimized intraocular lens (IOL) power calculation formulas. Methods: The study introduces analytical formulas to determine the optimal constant value for minimizing SD and RMS in single-optimized IOL power calculation formulas. These formulas were tested against various datasets containing biometric measurements from cataractous populations and included 10,330 eyes and 4 different IOL models. The study evaluated the effectiveness of the proposed method by comparing the outcomes with those obtained using traditional reference methods. Results: In optimizing IOL constants, minor differences between reference and estimated A-constants were found, with the maximum deviation at -0.086 (SD, SRK/T, and Vivinex) and -0.003 (RMS, PEARL DGS, and Vivinex). The largest discrepancy for third-generation formulas was -0.027 mm (SD, Haigis, and Vivinex) and 0.002 mm (RMS, Hoffer Q, and PCB00/SN60WF). Maximum RMS differences were -0.021 and +0.021, both involving Hoffer Q. Post-minimization, the largest mean prediction error was 0.726 diopters (D; SD) and 0.043 D (RMS), with the highest SD and RMS after adjustments at 0.529 D and 0.875 D, respectively, indicating effective minimization strategies. Conclusions: The study simplifies the process of minimizing SD and RMS in single-optimized IOL power predictions, offering a valuable tool for clinicians. However, it also underscores the complexity of achieving balanced optimization and suggests the need for further research in this area. Translational Relevance: The study presents a novel, clinically practical approach for optimizing IOL power calculations.